Ts.estim {TwoStepCLogit}

Two-Step Estimator

Description

Function that computes the two-step estimator proposed in Craiu et al. (2011) and its print method.

Usage

Ts.estim(formula, data, random, all.m.1 = FALSE, D = "UN(1)",
  itermax = 2000, tole = 1e-06)

## S3 method for class 'Ts.estim'
print(x, ...)

Arguments

formula

A formula object, with the response on the left of a ~ operator, and the covariates on the right. The right hand side of the model must also include two special terms: a strata and a cluster term (ex. formula = Y ~ X1 + X2 + X3 + strata(var_strata) + cluster(var_cluster)). The strata and cluster functions (from the package survival) are used to identify the stratification and the cluster variables, respectively.

data

A data frame (or object coercible by as.data.frame to a data frame) containing the variables in the model.

random

A formula object, with a blank on the left of a ~ operator, and, on the right, the covariates with random coefficients among the covariate listed in the model formula (ex. random = ~ X1 + X3). The default is to add random coefficients for every covariates listed in the model formula.

all.m.1

TRUE if sum of Y's in all strata is 1, FALSE otherwise (the default). When in doubt use FALSE (always works, but slower than necessary if all stratum sums are 1).

D

The form of the between-cluster variance-covariance matrix of the regression coefficients (matrix D) : either "UN" for unstructured matrix D or "UN(1)" (the default) for diagonal matrix D.

itermax

maximal number of EM iterations (default = 2000)

tole

maximal distance between successive EM iterations tolerated before declaring convergence (default = 0.000001)

x

An object, produced by the Ts.estim function, to print.

...

Further arguments to be passed to print.default.

Details

Calls coxph from the package survival.

Value

beta

A vector: the regression coefficients.

se

A vector: the regression coefficients' standard errors.

vcov

A matrix: the variance-covariance matrix of the regression coefficients.

D

A matrix: estimate of the between-cluster variance-covariance matrix of the regression coefficients (matrix D).

r.effect

The random effect estimates.

coxph.warn

A list of character string vectors. If the coxph function generates one or more warnings when fitting the Cox model to a cluster, a copy of these warnings are stored in coxph.warn$Cluster_name where Cluster_name is the identification value for the cluster in the data set. A NULL list element means that coxph did not produce any warnings for that cluster.

Call

The function call.

Author(s)

Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie Baillargeon

References

Craiu, R.V., Duchesne, T., Fortin, D. and Baillargeon, S. (2011), Conditional Logistic Regression with Longitudinal Follow-up and Individual-Level Random Coefficients: A Stable and Efficient Two-Step Estimation Method, Journal of Computational and Graphical Statistics. 20(3), 767-784.

See Also

ddim

Examples

# Two ways for specifying the same model
# Data: bison
# Model: covariates forest, biomass and pmeadow
# Random effects in front of forest and biomass
# Main diagonal covariance structure for D (the default)
way1 <- Ts.estim(formula = Y ~ forest  + biomass + pmeadow + 
        strata(Strata) + cluster(Cluster), data = bison, 
        random = ~ forest + biomass)
way1
way2 <- Ts.estim(formula = bison[,3] ~ as.matrix(bison[,c(6,8:9)]) + 
        strata(bison[,2]) + cluster(bison[,1]), data = bison, 
        random = ~ as.matrix(bison[,c(6,8)]))
way2

# Unstructured covariance for D
Fit <- Ts.estim(formula = Y ~ forest  + biomass + pmeadow + 
        strata(Strata) + cluster(Cluster), data = bison, 
        random = ~ forest + biomass, D="UN")
Fit